Abstract: A method for estimating a hydraulic state of a steam heating network during dynamic operation, the method comprising acquiring parameters, the parameters including steam flow G, steam flow velocity ?, steam density ?, steam pressure p, pipeline inner diameter D, pipeline inclination angle ?, a number of nodes N, and a number of branches M of each pipeline; inputting the parameters into a state estimation model constructed; and determining a hydraulic state by the state estimation model according to the parameters. The method and system for estimating a hydraulic state of a steam heating network during dynamic operation provided herein can adapt to dynamic working conditions of a steam network at project site, precisely estimate a hydraulic operation state of a steam network, and improve collection quality of hydraulic operation data so as to ensure that the network is in a safe operation state.

Abstract: The present invention relates to a clustering method based on iterations of neural networks, which comprises the following steps: step 1, initializing parameters of an extreme learning machine; step 2, randomly choosing samples of which number is equal to the number of clusters, each sample representing one cluster, forming an initial exemplar set and training the extreme learning machine; step 3, using current extreme learning machine to cluster samples, which generates a clustering result; step 4, choosing multiple samples from each cluster as exemplars for the cluster according to a rule; step 5, retraining the extreme learning machine by using the exemplars for each cluster obtained from step 4; and step 6, going back to step 3 to do iteration, otherwise obtaining and outputting clustering result until clustering result is steady or a maximal limit of the number of iterations is reached.

Abstract: A method of establishing a 3D saliency model based on 3D contrast and depth weight, includes dividing left view of 3D image pair into multiple regions by super-pixel segmentation method, synthesizing a set of features with color and disparity information to describe each region, and using color compactness as weight of disparity in region feature component, calculating feature contrast of a region to surrounding regions; obtaining background prior on depth of disparity map, and improving depth saliency through combining the background prior and the color compactness; taking Gaussian distance between the depth saliency and regions as weight of feature contrast, obtaining initial 3D saliency by adding the weight of the feature contrast; enhancing the initial 3D saliency by 2D saliency and central bias weight.

Abstract: A method of establishing a 3D saliency model based on 3D contrast and depth weight, includes dividing left view of 3D image pair into multiple regions by super-pixel segmentation method, synthesizing a set of features with color and disparity information to describe each region, and using color compactness as weight of disparity in region feature component, calculating feature contrast of a region to surrounding regions; obtaining background prior on depth of disparity map, and improving depth saliency through combining the background prior and the color compactness; taking Gaussian distance between the depth saliency and regions as weight of feature contrast, obtaining initial 3D saliency by adding the weight of the feature contrast; enhancing the initial 3D saliency by 2D saliency and central bias weight.

Abstract: The present invention relates to a clustering method based on iterations of neural networks, which comprises the following steps: step 1, initializing parameters of an extreme learning machine; step 2, randomly choosing samples of which number is equal to the number of clusters, each sample representing one cluster, forming an initial exemplar set and training the extreme learning machine; step 3, using current extreme learning machine to cluster samples, which generates a clustering result; step 4, choosing multiple samples from each cluster as exemplars for the cluster according to a rule; step 5, retraining the extreme learning machine by using the exemplars for each cluster obtained from step 4; and step 6, going back to step 3 to do iteration, otherwise obtaining and outputting clustering result until clustering result is steady or a maximal limit of the number of iterations is reached.

Abstract: The present invention discloses a method for detecting a salient region of a stereoscopic image, comprising: step 1) calculating flow information of each pixel separately with respect to a left-eye view and a right-eye view of the stereoscopic image; step 2) matching the flow information, to obtain a parallax map; step 3) selecting one of the left-eye view and the right-eye view, dividing it into T non-overlapping square image blocks; step 4) calculating a parallax effect value for each of the image blocks of the parallax map; step 5) for each of the image blocks of the selected one of the left-eye view and the right-eye view, calculating a central bias feature value and a spatial dissimilarity value, and multiplying the three values, to obtain a saliency value of the image block; and step 6) obtaining a saliency gray scale map of the stereoscopic image from saliency values of the image blocks.

Abstract: The present invention relates to a method for detecting visual saliencies of a video image based on spatial and temporal features, including: dividing an input image into image blocks and vectorizing the image blocks; decreasing dimensions of each image block through principal component analysis; calculating a dissimilarity between each image block and each of the other image blocks; calculating a visual saliency of each image block by combining a distance between image blocks, to obtain a spatial feature saliency map; imposing a central bias on the spatial feature saliency map; calculating a motion vector of each image block, extracting a temporal visual saliency of the current image by combining motion vectors of previous two frames, to obtain a temporal feature saliency map; integrating the spatial feature saliency map and the temporal feature saliency map to obtain a spatiotemporal feature saliency map, and smoothing the spatiotemporal feature saliency map to obtain a resulted image finally reflecting a s

Abstract: A method for embedding and extracting a multi-scale space based watermark, comprises: constructing a pyramid structure of an original image by dividing each carrier image layer into M square carrier image blocks of the same size; constructing a multi-scale structure of a watermark image; embedding a watermark by embedding each watermark image into a corresponding carrier image block to obtain the original image containing the watermark; locating in the pyramid structure of the original image a target image from which a watermark will be extracted; extracting the watermark by obtaining an estimated watermark by means of the target image block and the reference image block; comparing watermarks by evaluating similarity between the estimated watermark and a watermark image to which the reference image block corresponds. Due to the multi-resolution block pyramid data structure in the present invention, a large scale attack is decomposed into a multi-level small scale attack.

Abstract: The present invention relates to a method for detecting visual saliencies of a video image based on spatial and temporal features, including: dividing an input image into image blocks and vectorizing the image blocks; decreasing dimensions of each image block through principal component analysis; calculating a dissimilarity between each image block and each of the other image blocks; calculating a visual saliency of each image block by combining a distance between image blocks, to obtain a spatial feature saliency map; imposing a central bias on the spatial feature saliency map; calculating a motion vector of each image block, extracting a temporal visual saliency of the current image by combining motion vectors of previous two frames, to obtain a temporal feature saliency map; integrating the spatial feature saliency map and the temporal feature saliency map to obtain a spatiotemporal feature saliency map, and smoothing the spatiotemporal feature saliency map to obtain a resulted image finally reflecting a s

Abstract: The present invention discloses a method for detecting a salient region of a stereoscopic image, comprising: step 1) calculating flow information of each pixel separately with respect to a left-eye view and a right-eye view of the stereoscopic image; step 2) matching the flow information, to obtain a parallax map; step 3) selecting one of the left-eye view and the right-eye view, dividing it into T non-overlapping square image blocks; step 4) calculating a parallax effect value for each of the image blocks of the parallax map; step 5) for each of the image blocks of the selected one of the left-eye view and the right-eye view, calculating a central bias feature value and a spatial dissimilarity value, and multiplying the three values, to obtain a saliency value of the image block; and step 6) obtaining a saliency gray scale map of the stereoscopic image from saliency values of the image blocks.

Abstract: A method for embedding and extracting a multi-scale space based watermark, comprises: constructing a pyramid structure of an original image by dividing each carrier image layer into M square carrier image blocks of the same size; constructing a multi-scale structure of a watermark image; embedding a watermark by embedding each watermark image into a corresponding carrier image block to obtain the original image containing the watermark; locating in the pyramid structure of the original image a target image from which a watermark will be extracted; extracting the watermark by obtaining an estimated watermark by means of the target image block and the reference image block; comparing watermarks by evaluating similarity between the estimated watermark and a watermark image to which the reference image block corresponds. Due to the multi-resolution block pyramid data structure in the present invention, a large scale attack is decomposed into a multi-level small scale attack.

Abstract: The present invention discloses a method for retrieving a similar image based on visual saliencies and visual phrases, comprising: inputting an inquired image; calculating a saliency map of the inquired image; performing viewpoint shift on the saliency map by utilizing a viewpoint shift model, defining a saliency region as a circular region which taking a viewpoint as a center and R as a radius, and shifting the viewpoint for k times to obtain k saliency regions of the inquired image; extracting a visual word in each of the saliency regions of the inquired image, to constitute a visual phrase, and jointing k visual phrases to generate an image descriptor of the inquired image; obtaining an image descriptor for each image of an inquired image library; and calculating a similarity value between the inquired image and each image in the inquired image library depending on the image descriptors by utilizing a cosine similarity, to obtain an image similar to the inquired image from the inquired image library.

Abstract: The present invention relates to a method for selecting features of EEG signals based on a decision tree: firstly, acquired multi-channel EEG signals are pre-processed, and then the pre-processed EEG signals are performed with feature extraction by utilizing principal component analysis, to obtain a analysis data set matrix with decreased dimensions; superior column vectors are obtained through analyzing from the analysis data set matrix with decreased dimensions by utilizing a decision tree algorithm, and all the superior column vectors are jointed with the number of the columns increased and the number of the rows unchanged, to be reorganized into a final superior feature data matrix; finally, the reorganized superior feature data matrix is input to a support vector machine (SVM) classifier, to perform a classification on the EEG signals, to obtain a classification accuracy.